Image content moderation

ABSTRACT

In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

PRIORITY

This application is a Divisional of commonly assigned and co-pendingU.S. patent application Ser. No. 15/715,305, filed Sep. 26, 2017, whichclaims priority under 35 U.S.C. 119(a)-(d) to Indian patent applicationnumber 201711024017, having a filing date of Jul. 7, 2017, thedisclosures of which are hereby incorporated by reference in theirentireties.

BACKGROUND

An image may include various objects that are to be categorized and/ortagged. For example, an image may include content that is inappropriate.An image may include, objects, such as clothing articles that are to becategorized as male, female, neutral, etc. An image may also includeclothing articles that are to be tagged according to attributes such assize, color, etc.

BRIEF DESCRIPTION OF DRAWINGS

Features of the present disclosure are illustrated by way of example andnot limited in the following figure(s), in which like numerals indicatelike elements, in which:

FIG. 1 illustrates a layout of an image content moderation apparatus inaccordance with an example of the present disclosure;

FIG. 2 illustrates an example of inappropriate content tagging toillustrate operation of the image content moderation apparatus of FIG. 1in accordance with an example of the present disclosure;

FIG. 3 illustrates an example of category (e.g., gender) classificationto illustrate operation of the image content moderation apparatus ofFIG. 1 in accordance with an example of the present disclosure;

FIG. 4 illustrates an example of detailed tagging to illustrateoperation of the image content moderation apparatus of FIG. 1 inaccordance with an example of the present disclosure;

FIG. 5 illustrates predetermined classes that represent inappropriatecontent for inappropriate content tagging to illustrate operation of theimage content moderation apparatus of FIG. 1 in accordance with anexample of the present disclosure;

FIG. 6 illustrates region-based analysis for inappropriate contenttagging to illustrate operation of the image content moderationapparatus of FIG. 1 in accordance with an example of the presentdisclosure;

FIG. 7 illustrates blur detection for inappropriate content tagging toillustrate operation of the image content moderation apparatus of FIG. 1in accordance with an example of the present disclosure;

FIG. 8 illustrates inappropriate content tagging to illustrate operationof the image content moderation apparatus of FIG. 1 in accordance withan example of the present disclosure;

FIG. 9 illustrates an example of a convolutional neural network basedlearning model for category classification to illustrate operation ofthe image content moderation apparatus of FIG. 1 in accordance with anexample of the present disclosure;

FIG. 10 illustrates an example of category (e.g., gender) classificationto illustrate operation of the image content moderation apparatus ofFIG. 1 in accordance with an example of the present disclosure;

FIG. 11 illustrates an example of a convolutional neural network basedlearning model for detailed tagging to illustrate operation of the imagecontent moderation apparatus of FIG. 1 in accordance with an example ofthe present disclosure;

FIG. 12 illustrates an example of detailed tagging to illustrateoperation of the image content moderation apparatus of FIG. 1 inaccordance with an example of the present disclosure;

FIG. 13 illustrates an example of detailed tagging to illustrateoperation of the image content moderation apparatus of FIG. 1 inaccordance with an example of the present disclosure;

FIG. 14 illustrates an example of detailed tagging to illustrateoperation of the image content moderation apparatus of FIG. 1 inaccordance with an example of the present disclosure;

FIG. 15 illustrates an example block diagram for image contentmoderation in accordance with an example of the present disclosure;

FIG. 16 illustrates a flowchart of an example method for image contentmoderation in accordance with an example of the present disclosure; and

FIG. 17 illustrates a further example block diagram for image contentmoderation in accordance with another example of the present disclosure.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to examples. In the following description,numerous specific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be readily apparenthowever, that the present disclosure may be practiced without limitationto these specific details. In other instances, some methods andstructures have not been described in detail so as not to unnecessarilyobscure the present disclosure.

Throughout the present disclosure, the terms “a” and “an” are intendedto denote at least one of a particular element. As used herein, the term“includes” means includes but not limited to, the term “including” meansincluding but not limited to. The term “based on” means based at leastin part on.

Image content moderation apparatuses, methods for image contentmoderation, and non-transitory computer readable media having storedthereon machine readable instructions to provide image contentmoderation are disclosed herein. The apparatuses, methods, andnon-transitory computer readable media disclosed herein provide forinappropriate content tagging, category classification, and/or detailedtagging of an object in an image. Inappropriate content tagging mayinclude identification of unauthorized content, and tagging of theunauthorized content as inappropriate. Category classification mayinclude, for example, assignment of a gender to an object. For example,a gender such as male, female, or neutral may be assigned to an objectsuch as a clothing product. With respect to detailed tagging, an object,such as a clothing article, may be tagged with respect to attributessuch as size, color, fabric, etc.

Image content moderation may refer to the practice of evaluating imagesfor illegal or unwanted content in order to make decisions about what isacceptable and what is not. In this regard, inappropriate products suchas weapons, safety equipment, seeds, etc., may be disallowed in certainimages. Similarly, product images that may be blurred, images ofcelebrities, counterfeited brand images, etc., may be disallowed. Imagecontent moderation may also include categorization and detailed taggingof products.

With respect to categorization and detailed tagging of products, in ane-commerce application, products may be sold online through websites. Inorder to sell products through a website, a vendor may upload images ofproducts that need to be moderated before being posted on the website.The content moderation of the products may be based on polices and rulesspecified by an e-commerce company that is to sell the products online.

It is technically challenging to determine whether content of an imageis appropriate or inappropriate, in view of the various types of contentthat may be present in an image. It is technically challenging toclassify products according to categories, for example, on e-commercemarketplace websites, with respect gender. For example, face recognitionmay be used to guess as to whether a product is to be categorized asmale or female. However, products at e-commerce websites may bedisplayed by persons whose gender (e.g., male/female) may differ fromthe gender of the product (e.g., female/male). Further, it istechnically challenging to tag products, for example, on e-commercemarketplace websites, with respect attributes of the products. Forexample, a product on an e-commerce website may need to be tagged withrespect to color information, category of the product, fiber used inproduct, etc.

In order to address at least these technical challenges with respect toinappropriate content tagging, classification of objects according tocategories, and detailed tagging of objects, such as products, onimages, such as those included on e-commerce websites, the image contentmoderation as disclosed herein provides for inappropriate contenttagging, classification of objects according to categories, and detailedtagging of objects.

With respect to inappropriate content tagging, the image contentmoderation as disclosed herein may include ascertaining (e.g., by aninappropriate content tagger) an image. The image content moderation asdisclosed herein may further include determining, for the image, aplurality of predetermined classes that represent inappropriate content,and identifying an object displayed in the image. The image contentmoderation as disclosed herein may further include analyzing the objectwith respect to each class of the plurality of predetermined classes,and classifying, based on the analysis of the object with respect toeach class of the plurality of predetermined classes, the object asappropriate or inappropriate. The image content moderation as disclosedherein may further include determining whether the object is classifiedas inappropriate, and in response to a determination that the object isclassified as inappropriate, marking the object as inappropriate.

With respect to classification of objects according to categories, theimage content moderation as disclosed herein may include classifying(e.g., by an object category classifier), based on a learning model, anobject displayed in an image into a category of a plurality ofcategories. The image content moderation as disclosed herein may furtherinclude detecting (e.g., by an object category detector), based onanother learning model, the object displayed in the image, refining(e.g., by the object category detector) the detected object based on alabel associated with the detected object, and determining (e.g., by theobject category detector), based on the another learning model, acategory of the plurality of categories for the refined detected object.The image content moderation as disclosed herein may further includeidentifying (e.g., by a keyword category analyzer), based on the label,a keyword associated with the object displayed in the image from aplurality of keywords, and determining (e.g., by the keyword categoryanalyzer), based on the identified keyword, of a category of theplurality of categories for the object displayed in the image. The imagecontent moderation as disclosed herein may further include categorizing(e.g., by a rules-based categorizer), based on a set of rules, theobject displayed in the image into a category of the plurality ofcategories. Further, the image content moderation as disclosed hereinmay include moderating of image content of the image by categorizing(e.g., by a fusion-based categorizer), based on the classification ofthe object category classifier, the determination of the object categorydetector, the determination of the keyword category analyzer, and thecategorization of the rules-based categorizer, the object displayed inthe image into a category of the plurality of categories.

With respect to detailed tagging of objects, the image contentmoderation as disclosed herein may include classifying (e.g., by anobject tagging classifier), based on a learning model, an objectdisplayed in an image into a category of a plurality of categories. Theimage content moderation as disclosed herein may further includedetermining (e.g., by a color tagger), color information from theobject, a plurality of color tags associated with the object, aplurality of color distances between the color information and theplurality of color tags, and a color tag of the plurality of color tagsthat is to be assigned to the object based on a determination of aminimum color distance from the plurality of color distances. The colortag may represent a color that is to be assigned to the object. Theimage content moderation as disclosed herein may further includedetermining (e.g., by a keyword tagging analyzer), a category and acolor associated with the object based on a label associated with theobject. Further, the image content moderation as disclosed herein mayinclude moderating (e.g., by a fusion-based tagger), image content ofthe image by tagging, based on the classification of the object taggingclassifier, the determination of the color tagger, and the determinationof the keyword tagging analyzer, the object displayed in the image witha category and a color associated with the object.

The apparatuses, methods, and non-transitory computer readable mediadisclosed herein provide for content moderation as a service. In thisregard, the apparatuses, methods, and non-transitory computer readablemedia disclosed herein may be trained to understand a moderation queueand provide tagging of content based, for example, on machine learningand deep learning techniques for tagging the queues. The apparatuses,methods, and non-transitory computer readable media disclosed herein mayalso provide for tagging based, for example, on text fields, images, andvideos with guidelines provided by the end user.

For the apparatuses, methods, and non-transitory computer readable mediadisclosed herein, the elements of the apparatuses, methods, andnon-transitory computer readable media disclosed herein may be anycombination of hardware and programming to implement the functionalitiesof the respective elements. In some examples described herein, thecombinations of hardware and programming may be implemented in a numberof different ways. For example, the programming for the elements may beprocessor executable instructions stored on a non-transitorymachine-readable storage medium and the hardware for the elements mayinclude a processing resource to execute those instructions. In theseexamples, a computing device implementing such elements may include themachine-readable storage medium storing the instructions and theprocessing resource to execute the instructions, or the machine-readablestorage medium may be separately stored and accessible by the computingdevice and the processing resource. In some examples, some elements maybe implemented in circuitry.

FIG. 1 illustrates a layout of an example image content moderationapparatus (hereinafter also referred to as “apparatus 100”).

Referring to FIG. 1, with respect to inappropriate content tagging, theapparatus 100 may include an inappropriate content tagger 102, which isexecuted by at least one hardware processor (e.g., the processor 1502 ofFIG. 15 or the processor 1704 of FIG. 17), to ascertain an image 104.

The inappropriate content tagger 102 is to determine, for the image 104,a plurality of predetermined classes 106 that represent inappropriatecontent.

The inappropriate content tagger 102 is to identify an object 108displayed in the image 104.

The inappropriate content tagger 102 is to analyze the object 108 withrespect to each class of the plurality of predetermined classes 106.

The inappropriate content tagger 102 is to classify, based on theanalysis of the object 108 with respect to each class of the pluralityof predetermined classes 106, the object 108 as appropriate orinappropriate.

The inappropriate content tagger 102 is to determine whether the object108 is classified as inappropriate.

In response to a determination that the object 108 is classified asinappropriate, the inappropriate content tagger 102 is to mark theobject 108 as inappropriate.

According to an example, the inappropriate content tagger 102 is totrain a learning model 110 to analyze a region of the object 108, andanalyze, based on the learning model 110, the object 108 with respect toeach class of the plurality of predetermined classes 106.

According to an example, the inappropriate content tagger 102 is todetect a face of a person in the object 108, transform an image of thedetected face into frequency domain, and determine an energy componentof the transformed image of the detected face. Further, theinappropriate content tagger 102 is to compare the determined energycomponent to a threshold, and in response to a determination that theenergy component is below the threshold, classify the object 108 asinappropriate.

According to an example, the inappropriate content tagger 102 is toascertain a list of defaulter vendors 112, and compare a vendorassociated with the inappropriate object with the list of defaultervendors 112. In response to a determination that the vendor associatedwith the inappropriate object is on the list of defaulter vendors 112,the inappropriate content tagger 102 is to increase a defaulterprobability associated with the vendor associated with the inappropriateobject. Further, the inappropriate content tagger 102 is to compare atotal probability based on the increase in the defaulter probability toa threshold, and in response to a determination that the totalprobability is greater than the threshold, the inappropriate contenttagger 102 is to mark the vendor associated with the inappropriateobject as being unauthorized to upload further images.

Referring to FIG. 1, with respect to category classification, theapparatus 100 may include an object category classifier 114, which isexecuted by at least one hardware processor (e.g., the processor 1502 ofFIG. 15 or the processor 1704 of FIG. 17), to classify, based on alearning model 116, the object 108 displayed in the image 104 into acategory of a plurality of categories 118. According to an example, thelearning model 116 used by the object category classifier 114 mayinclude a convolutional neural network based deep learning model.According to an example, the plurality of categories 118 may includemale, female, and neutral. Further, according to an example, the object108 may include a clothing product.

According to an example, the object category classifier 114 is to trainthe learning model 116 used by the object category classifier using aplurality of categorized images. Once the learning model 116 is trained,the object category classifier 114 is to classify, based on the trainedlearning model used by the object category classifier 114, the object108 displayed in the image 104 into the category of the plurality ofcategories 118.

With respect to category classification, the apparatus 100 may includean object category detector 120, which is executed by the at least onehardware processor (e.g., the processor 1502 of FIG. 15 or the processor1704 of FIG. 17) to detect, based on another learning model 122, theobject 108 displayed in the image 104. Further, the object categorydetector 120 is to refine the detected object based on a label 124associated with the detected object. Further, the object categorydetector 120 is to determine, based on the another learning model 122, acategory of the plurality of categories 118 for the refined detectedobject. In this regard, the category of the plurality of categories 118determined by the object category detector 120 may be the same categoryor a different category as determined by the object category classifier114.

According to an example, the object category detector 120 is to refinethe detected object based on the label 124 that includes a text label.

With respect to category classification, the apparatus 100 may include akeyword category analyzer 126, which is executed by the at least onehardware processor (e.g., the processor 1502 of FIG. 15 or the processor1704 of FIG. 17) to identify, based on the label 124, a keywordassociated with the object 108 displayed in the image 104 from aplurality of keywords 128. Further, the keyword category analyzer 126 isto determine, based on the identified keyword, a category of theplurality of categories 118 for the object 108 displayed in the image104. In this regard, the category of the plurality of categories 118determined by the keyword category analyzer 126 may be the same categoryor a different category as determined by the object category classifier114 and/or the object category detector 120.

With respect to category classification, the apparatus 100 may include arules-based categorizer 130, which is executed by the at least onehardware processor (e.g., the processor 1502 of FIG. 15 or the processor1704 of FIG. 17) to categorize, based on a set of rules 132, the object108 displayed in the image 104 into a category of the plurality ofcategories 118. In this regard, the category of the plurality ofcategories 118 determined by the rules-based categorizer 130 may be thesame category or a different category as determined by the objectcategory classifier 114, the object category detector 120, and/or thekeyword category analyzer 126.

With respect to category classification, the apparatus 100 may include afusion-based categorizer 134, which is executed by the at least onehardware processor (e.g., the processor 1502 of FIG. 15 or the processor1704 of FIG. 17) to moderate image content of the image 104 bycategorizing, based on the classification of the object categoryclassifier 114, the determination of the object category detector 120,the determination of the keyword category analyzer 126, and thecategorization of the rules-based categorizer 130, the object 108displayed in the image 104 into a category 136 of the plurality ofcategories 118. In this regard, the category 136 determined by thefusion-based categorizer 134 may be one of the categories determined bythe object category classifier 114, the object category detector 120,the keyword category analyzer 126, and the rules-based categorizer 130.

According to an example, the fusion-based categorizer 134 is to moderatethe image content of the image 104 by categorizing, based on a majoritydecision associated with the classification of the object categoryclassifier 114, the determination of the object category detector 120,the determination of the keyword category analyzer 126, and thecategorization of the rules-based categorizer 130, the object 108displayed in the image 104 into the category 136 of the plurality ofcategories 118.

With respect to detailed tagging, the apparatus 100 may include anobject tagging classifier 138, which is executed by at least onehardware processor (e.g., the processor 1502 of FIG. 15 or the processor1704 of FIG. 17), to classify, based on a learning model 140, the object108 displayed in the image 104 into a category of a plurality ofcategories 142. In this regard, the categories 142 may be the same ordifferent compared to the categories 118. For example, the categories118 may include male, female, and neutral, and the categories 142 mayinclude categories 1-10 that represent different sizes of clothes.

According to an example, the object tagging classifier 138 is toclassify, based on the learning model 140, the object 108 displayed inthe image 104 into the category of the plurality of categories 142, bytraining the learning model 140 using a plurality of categorized images,and classifying, based on the trained learning model, the object 108into the category of the plurality of categories 142.

With respect to detailed tagging, the apparatus 100 may include a colortagger 144, which is executed by at least one hardware processor (e.g.,the processor 1502 of FIG. 15 or the processor 1704 of FIG. 17), todetermine color information 146 from the object 108, and a plurality ofcolor tags 148 associated with the object 108. Further, the color tagger144 is to determine a plurality of color distances between the colorinformation 146 and the plurality of color tags 148. Yet further, thecolor tagger 144 is to determine a color tag of the plurality of colortags 148 that is to be assigned to the object 108 based on adetermination of a minimum color distance from the plurality of colordistances. In this regard, the color tag may represent a color that isto be assigned to the object 108.

With respect to detailed tagging, the apparatus 100 may include akeyword tagging analyzer 150, which is executed by at least one hardwareprocessor (e.g., the processor 1502 of FIG. 15 or the processor 1704 ofFIG. 17), to determine a category and a color associated with the object108 based on the label 124 associated with the object 108. In thisregard, the category and color determined by the keyword tagginganalyzer 150 may be the same or different compared to the categorydetermined by the object tagging classifier 138 and/or the colordetermined by the color tagger 144.

According to an example, the keyword tagging analyzer 150 is to refinethe category classification by the object tagging classifier 138 and thecolor determination by the color tagger 144. In this regard, thecategory and color determination by the keyword tagging analyzer 150 maybe used as a secondary or supplemental determination compared to thedetermination by the object tagging classifier 138 and the color tagger144.

With respect to detailed tagging, the apparatus 100 may include afusion-based tagger 152, which is executed by at least one hardwareprocessor (e.g., the processor 1502 of FIG. 15 or the processor 1704 ofFIG. 17), to moderate image content of the image 104 by tagging, basedon the classification of the object tagging classifier 138, thedetermination of the color tagger 144, and the determination of thekeyword tagging analyzer 150, the object 108 displayed in the image 104with a category 154 and a color 156 associated with the object 108. Inthis regard, the category 154 and color 156 tagged by the fusion-basedtagger 152 may be one of the categories determined by the object taggingclassifier 138 and the keyword tagging analyzer 150, and one of thecolors determined by the color tagger 144 and the keyword tagginganalyzer 150.

According to an example, the fusion-based tagger 152 is to moderate,based on a majority decision associated with the classification of theobject tagging classifier 138, the determination of the color tagger144, and the determination of the keyword tagging analyzer 150, theobject 108 displayed in the image 104 with the category and the colorassociated with the object 108.

FIG. 2 illustrates an example of inappropriate content tagging toillustrate operation of the apparatus 100 in accordance with an exampleof the present disclosure.

Referring to FIG. 2, with respect to inappropriate content tagging,e-commerce marketplace websites may need to disallow (referred asin-appropriate) certain types of products (e.g., weapons, safetyequipment, seeds, etc.), as well as certain types of product images(e.g., blurred images, celebrity images, counterfeited brand images,etc.) to be sold from there website. In this regard, the apparatus 100may detect these inappropriate products uploaded by vendors.

FIG. 3 illustrates an example of category (e.g., gender) classificationto illustrate operation of the apparatus 100 in accordance with anexample of the present disclosure.

Referring to FIG. 3, the apparatuses, methods, and non-transitorycomputer readable media disclosed herein provide for categoryclassification, for example, for e-commerce marketplace websites whichmay need the products to be classified into three genders: male, female,and neutral. In this regard, the apparatuses, methods, andnon-transitory computer readable media disclosed herein provide forcategory classification of uploaded images of products, for example,into three genders: male, female, or neutral.

FIG. 4 illustrates an example of detailed tagging to illustrateoperation of the apparatus 100 in accordance with an example of thepresent disclosure.

Referring to FIG. 4, the apparatuses, methods, and non-transitorycomputer readable media disclosed herein provide for detailed tagging,for example, for e-commerce marketplace websites of the detailedinformation of products, such as, color information, category of theproduct, fiber used in product, etc. In this regard, the apparatuses,methods, and non-transitory computer readable media disclosed hereinprovide for detailed tagging of detailed information of a product basedon image and other specifications available with the uploaded products.

FIG. 5 illustrates predetermined classes that represent inappropriatecontent for inappropriate content tagging to illustrate operation of theapparatus 100 in accordance with an example of the present disclosure.

Referring to FIG. 5, for the predetermined classes 106, each uploadedimage corresponding to a product may undergo content moderation. Aproduct is to be tagged as inappropriate if even one uploaded imagecould be classified as inappropriate. Examples of reasons to tag aproduct as inappropriate are shown in FIG. 5 and include “counterfeit ofa major brand”, “blurred out tag or label”, etc.

FIG. 6 illustrates region-based analysis for inappropriate contenttagging to illustrate operation of the apparatus 100 in accordance withan example of the present disclosure.

Referring to FIG. 6, as disclosed herein, the inappropriate contenttagger 102 is to train a learning model 110 to analyze a region of theobject 108, and analyze, based on the learning model 110, the object 108with respect to each class of the plurality of predetermined classes106. In this regard, the inappropriate content tagger 102 may include aclassifier corresponding to each reason (for inappropriate content) andallow each image of a product to pass through each of the classes. Animage may be classified into one or more inappropriate classes. Theimage may be tagged with the classified one or more inappropriatereasons. Further, a region based convolutional neural network may betrained for classifying an image (or an object of the image) into aninappropriate category. The trained learning model 110 may track theinappropriate reason present in the image even at very partial amounts.For example, as shown in FIG. 6, a logo of a brand that is inappropriateis detected in the image even if the logo is present in a relativelysmall amount.

FIG. 7 illustrates blur detection for inappropriate content tagging toillustrate operation of the apparatus 100 in accordance with an exampleof the present disclosure.

Referring to FIG. 7, as disclosed herein, the inappropriate contenttagger 102 is to detect a face of a person in the object 108, transforman image of the detected face into frequency domain, and determine anenergy component of the transformed image of the detected face. Further,the inappropriate content tagger 102 is to compare the determined energycomponent to a threshold, and in response to a determination that theenergy component is below the threshold, classify the object 108 asinappropriate. In this regard, once the person's face is detected in theimage, an image transformation technique may be used to transform theface image into the frequency domain. For example, Laplace and Fouriertransformation may be used to transform the face image into thefrequency domain. The energy component of the face image may then bedetermined and compared with an empirically chosen threshold to depictthe presence of blur at the face image.

With respect to the defaulter vendors 112, as disclosed herein, theinappropriate content tagger 102 is to ascertain a list of defaultervendors 112, and compare a vendor associated with the inappropriateobject with the list of defaulter vendors 112. In response to adetermination that the vendor associated with the inappropriate objectis on the list of defaulter vendors 112, the inappropriate contenttagger 102 is to increase a defaulter probability associated with thevendor associated with the inappropriate object. Further, theinappropriate content tagger 102 is to compare a total probability basedon the increase in the defaulter probability to a threshold, and inresponse to a determination that the total probability is greater thanthe threshold, the inappropriate content tagger 102 is to mark thevendor associated with the inappropriate object as being unauthorized toupload further images. In this regard, once a product is classified asinappropriate, a checklist may be specified of the inappropriateproduct, and the corresponding vendor. The next time when theinappropriate product is received from the defaulter vendor, a definedprobability may be added to the defaulter vendor. Hence, at a time ifthe probability of defaulter vendor increases with some threshold, thevendor may be discarded or otherwise unauthorized to upload products atan associated portal.

FIG. 8 illustrates inappropriate content tagging to illustrate operationof the apparatus 100 in accordance with an example of the presentdisclosure.

Referring to FIG. 8, the apparatus 100 may be implemented, for example,as a CHROME™ extension to provide a seamless end to end ‘inappropriatetagging’ mechanism. The extension may extract the product-label (e.g.,text information available with the product) and image-URLscorresponding to each product. In this case, more than one image may beassociated with one product. Further, the extension may send theextracted text and image-URLs to a server for computation. The results(e.g., classes of inappropriate categories) for each product may then betagged back to the web-page by selecting the available two inappropriatetags as shown in FIG. 8.

FIG. 9 illustrates an example of a convolutional neural network basedlearning model for category classification to illustrate operation ofthe apparatus 100 in accordance with an example of the presentdisclosure.

Referring to FIGS. 1 and 9, with respect to category classification, theobject category classifier 114, which is executed by the at least onehardware processor (e.g., the processor 1502 of FIG. 15 or the processor1704 of FIG. 17), is to classify, based on the learning model 116, theobject 108 displayed in the image 104 into a category of the pluralityof categories 118. In this regard, the learning model 116 may include aconvolutional neural network based deep learning model, which may betrained for image classification. According to an example, the learningmodel 116 may include eight deep layers as shown in FIG. 9. The eightdeep layers may include five convolutional layers, two fully connectedlayers, and one classification layer. The learning model 116 may betrained on images of a number of products (as per a list of productsshared by an e-commerce provider) for gender classification.

As disclosed herein, the object category detector 120, which is executedby the at least one hardware processor (e.g., the processor 1502 of FIG.15 or the processor 1704 of FIG. 17) is to detect, based on anotherlearning model 122, the object 108 displayed in the image 104. Further,the object category detector 120 is to refine the detected object basedon the label 124 associated with the detected object, and determine,based on the another learning model 122, a category of the plurality ofcategories 118 for the refined detected object. For example, the objectcategory detector 120 may use a deep learning object detection model.The detected objects may then be refined using the label 124 availablewith the products. The learning model 122 may then provide the gender ofthe extracted product.

As disclosed herein, the keyword category analyzer 126, which isexecuted by the at least one hardware processor (e.g., the processor1502 of FIG. 15 or the processor 1704 of FIG. 17) is to identify, basedon the label 124, a keyword associated with the object 108 displayed inthe image 104 from a plurality of keywords 128. Further, the keywordcategory analyzer 126 is to determine, based on the identified keyword,a category of the plurality of categories 118 for the object 108displayed in the image 104. In this regard, the keyword categoryanalyzer 126 may utilize product-labels (such as: “Women OL BusinessBlazer Suit Long Sleeve Casual Tops Slim Jacket Coat Outwear”), andprocess the labels to determine the gender of the product. A number ofkeywords associated with genders (male/female/neutral) may be formed,and whenever one of the keywords appears in the product-label, thekeyword category analyzer 126 may use the identified keyword to providethe gender of the product. For example, the keywords at theaforementioned product-label may hint for the product as being for afemale.

As disclosed herein, the rules-based categorizer 130, which is executedby the at least one hardware processor (e.g., the processor 1502 of FIG.15 or the processor 1704 of FIG. 17) is to categorize, based on a set ofrules 132, the object 108 displayed in the image 104 into a category ofthe plurality of categories 118. In this regard, the rules-basedcategorizer 130 may utilize the product-list and other rules provided,for example, by an e-commerce client, for gender classification. Withrespect to rules for gender classification of products, a rule mayspecify that children's products should always be classified as neutral.Based on the available rules, products may be divided into “hard” and“soft” categories. In the hard category, products may categorized asmale/female/neutral based on a rule. For example, children's productsshould always be categorized into the neutral gender. For the softcategory, a rule may not be available to categorize the product gender,and thus, the decision to categorize a product in a soft category may bebased on the results, for example, of the object category classifier114, the object category detector 120, and/or the keyword categoryanalyzer 126.

FIG. 10 illustrates an example of category (e.g., gender) classificationto illustrate operation of the apparatus 100 in accordance with anexample of the present disclosure.

Referring to FIG. 10, as disclosed herein, the fusion-based categorizer134, which is executed by the at least one hardware processor (e.g., theprocessor 1502 of FIG. 15 or the processor 1704 of FIG. 17) is tomoderate image content of the image 104 by categorizing, based on theclassification of the object category classifier 114, the determinationof the object category detector 120, the determination of the keywordcategory analyzer 126, and the categorization of the rules-basedcategorizer 130, the object 108 displayed in the image 104 into acategory 136 of the plurality of categories 118. In this regard, thefusion-based categorizer 134 is to generate a final categorizationdecision based on a combination of the classification of the objectcategory classifier 114, the determination of the object categorydetector 120, the determination of the keyword category analyzer 126,and the categorization of the rules-based categorizer 130. For example,the fusion-based categorizer 134 may implement a majority voting scheme.

Referring again to FIG. 10, the apparatus 100 may be implemented, forexample, as a CHROME™ extension to provide a seamless end to end ‘genderclassification’ mechanism. The extension may extract the product-label(e.g., text information available with the product) and image-UniformResource Locators (URLs) corresponding to each product. In this case,more than one image may be associated with one product. Further, theextension may send the extracted text and image-URLs to a server forcomputation. The results (e.g., classes of images: male/female/neutral)for each product may then be tagged back to the web-page by selectingthe available two tags: male or female as shown in FIG. 10, or male,female, or neutral if three tags are available.

FIG. 11 illustrates an example of a convolutional neural network basedlearning model for detailed tagging to illustrate operation of theapparatus 100 in accordance with an example of the present disclosure.

Referring to FIG. 11, as disclosed herein, the object tagging classifier138, which is executed by at least one hardware processor (e.g., theprocessor 1502 of FIG. 15 or the processor 1704 of FIG. 17), is toclassify, based on the learning model 140, the object 108 displayed inthe image 104 into a category of the plurality of categories 142. Inthis regard, the learning model 140 may include a convolutional neuralnetwork based deep learning model that is trained on images of products,for example, from 1000 unique categories. The trained learning model 140may classify the input images among one of the categories. As shown inFIG. 11, the learning model 140 may include eight deep layers, includingfive convolutional layers, two fully connected layers, and oneclassification layer.

FIG. 12 illustrates an example of detailed tagging to illustrateoperation of the apparatus 100 in accordance with an example of thepresent disclosure.

Referring to FIG. 12, as disclosed herein, the color tagger 144, whichis executed by at least one hardware processor (e.g., the processor 1502of FIG. 15 or the processor 1704 of FIG. 17), is to determine colorinformation 146 from the object 108, and a plurality of color tags 148associated with the object 108. Further, the color tagger 144 is todetermine a plurality of color distances between the color information146 and the plurality of color tags 148. Yet further, the color tagger144 is to determine a color tag (e.g., “purple” for the example of FIG.12) of the plurality of color tags 148 that is to be assigned to theobject 108 based on a determination of a minimum color distance from theplurality of color distances. In this regard, the color tagger 144 maydetect the object (or location) from the image in the presence ofvarious other objects (or locations). The color tagger 144 may thendetect the color from the object (or location). The color tagger 144 mayutilize K-means clustering for detecting the color of a product. Thedetected color may be compared with the available color tags and thenearest color may be tagged for the object. As discussed below, thebackground color of the images may dominate the color extractionprocess, and hence in some cases may lead to an incorrect colorextraction from the object. In this case, as discussed below, ahistogram based technique may be implemented to remove background colorfrom the extracted color of the object. The color tagger 144 may thusprovide the color of the product present in the image.

In further detail, the color tagger 144 is to ascertain, for the image104 that is to be analyzed, an attribute of the image 104. Further, thecolor tagger 144 is to determine, based on the attribute of the image104, a target object (e.g., the object 108) that is to be identified andcolor tagged in the image 104. According to an example, the attribute ofthe image 104 may include audible and/or visible attributes associatedwith the image 104. According to an example, the target object mayinclude an element and/or a region of the image 104.

The color tagger 144 is to extract, based on a learning model, aplurality of objects from the image 104. Further, the color tagger 144is to identify, based on a comparison of the target object that is to beidentified and color tagged in the image 104 and the plurality ofextracted objects from the image, the target object in the image 104.

The color tagger 144 is to extract the color information 146 from theidentified target object. The color tagger 144 is to ascertain aplurality of color tags 148 associated with the identified targetobject. Further, the color tagger 144 is to determine a plurality ofcolor distances between the color information 146 and the plurality ofcolor tags 148.

The color tagger 144 is to determine, based on a determination of aminimum color distance from the plurality of color distances, a colortag of the plurality of color tags 148 that is to be assigned to theidentified target object.

The color tagger 144 is to determine whether background color (i.e.,color of the image other than the color of objects in the image, orcolor of the image outside of the boundaries of the target object)should be removed from the image 104. In this regard, the analysis bythe color tagger 144 may be performed prior to extraction of the colorinformation from the identified target object. With respect todetermining whether background color should be removed from the image104, the color tagger 144 may determine a histogram of a plurality ofcolor clusters, where the color clusters are determined from the entireimage 104 (e.g., the extracted color information from the identifiedtarget object and color information from a remaining portion of theimage 104). The plurality of color clusters may be determined, forexample, by using k-means clustering. The color tagger 144 may sorthistogram bins associated with the determined histogram of the pluralityof color clusters in descending order. The color tagger 144 maydetermine a difference between a highest order bin and a subsequent binof the sorted histogram bins. The difference may represent a differencebetween background color and a color of an object. The highest order binmay represent a bin which includes a highest count of color occurrencesfor a particular color in the image 104, the subsequent bin mayrepresent a bin which includes the second highest count of coloroccurrences for the particular color in the image 104, and so forth. Thecolor tagger 144 may determine whether the difference is greater than aspecified threshold. For example, the specified threshold may be 40%. Inthis regard, in response to a determination that the difference isgreater than the specified threshold, the color tagger 144 may removebackground color from the image 104. Otherwise, in response to adetermination that the difference is less than the specified threshold,the color tagger 144 may not remove background color from the image 104.In this manner, background color interference with respect to extractionof the color information 146 from the identified target object may beminimized.

According to an example, the attribute of the image 104 may include textassociated with the image 104. In this regard, the color tagger 144 isto determine, based on the text associated with the image, the targetobject that is to be identified and color tagged in the image 104 bydetermining, based on an analysis of a repository of available objectsbased on the text associated with the image, types of related objects.According to an example, the types of related objects may include thetarget object that is to be identified and color tagged in the image104. Further, the color tagger 144 is to identify, based on thecomparison of the types of related objects and the plurality ofextracted objects from the image, the target object in the image 104.According to an example, the types of related objects may be determinedas a function of synonyms determined from the text associated with theimage 104.

According to an example, the color tagger 144 is to extract colorinformation from the identified target object by applying k-meansclustering.

According to an example, the color tagger 144 is to determine theplurality of color distances between the color information 146 and theplurality of color tags 148 by determining values of L*C*h for each ofthe plurality of color tags. In this regard, L* may represent lightness,C* may represent chroma, and h may represent a hue angle. The colortagger 144 may also determine values of L*C*h for the extracted colorinformation. Further, the color tagger 144 may determine, based on theL*C*h values for each of the plurality of color tags and the L*C*hvalues for the extracted color information, the plurality of colordistances between the color information 146 and the plurality of colortags 148.

According to an example, the color tagger 144 is to determine theplurality of color distances between the color information and theplurality of color tags by determining CIEDE2000 color distances betweenthe color information and the plurality of color tags.

FIG. 13 illustrates an example of detailed tagging to illustrateoperation of the apparatus 100 in accordance with an example of thepresent disclosure.

Referring to FIG. 13, as disclosed herein, the keyword tagging analyzer150, which is executed by at least one hardware processor (e.g., theprocessor 1502 of FIG. 15 or the processor 1704 of FIG. 17), is todetermine a category and a color associated with the object 108 based onthe label 124 associated with the object 108. In this regard, a productmay be uploaded by vendors with product information (e.g., text/label),which may represent the possible description of the product. For theexample of FIG. 13, the product information may include “Womens ShortSleeved Chiffon”, etc. The keyword tagging analyzer 150 may utilize apre-trained model such as WORDNET™ and/or WORD2VEC™ to extract the mostrepresentative keywords from the available product description. Thus,the keyword tagging analyzer 150 may predict the category and color ofthe product in an image, and refine the tags provided by the objecttagging classifier 138 and/or the color tagger 144 to determine the mostrepresentative tags for the category and color of the product. Theresults of the keyword tagging analyzer 150 may also be utilized in aweighted combination with respect to the object tagging classifier 138and the color tagger 144. For example, the results of the object taggingclassifier 138 may be assigned a weight W1, the results of the colortagger 144 may be assigned a weight W2, and the results of the keywordtagging analyzer 150 may be assigned a weight W3. In this manner, thecategory and/or color associated with the highest weight may be selectedby the fusion-based tagger 152. For the example of FIG. 13, the colormay be determined as purple, and the category may be determined as“short sleeves”.

FIG. 14 illustrates an example of detailed tagging to illustrateoperation of the apparatus 100 in accordance with an example of thepresent disclosure.

Referring to FIG. 14, the fusion-based tagger 152, which is executed byat least one hardware processor (e.g., the processor 1502 of FIG. 15 orthe processor 1704 of FIG. 17), is to moderate image content of theimage 104 by tagging, based on the classification of the object taggingclassifier 138, the determination of the color tagger 144, and thedetermination of the keyword tagging analyzer 150, the object 108displayed in the image 104 with a category 154 and a color 156associated with the object 108.

Referring again to FIG. 14, the apparatus 100 may be implemented, forexample, as a CHROME™ extension to provide a seamless end to end‘detailed tagging’ mechanism. The extension may extract theproduct-label (e.g., text information available with the product) andimage-URLs corresponding to each product. In this regard, more than oneimage may be associated with one product. The extension may further sendthe extracted text and image-URLs to a server for computation. Further,the results (product category and color) for each product may be taggedback to the web-page by selecting the available tags, as shown in FIG.14. In this regard, FIG. 14 shows gender tagging (male/female/neutral),and color tagging.

FIGS. 15-17 respectively illustrate an example block diagram 1500, aflowchart of an example method 1600, and a further example block diagram1700 for image content moderation, according to examples. The blockdiagram 1500, the method 1600, and the block diagram 1700 may beimplemented on the apparatus 100 described above with reference to FIG.1 by way of example and not of limitation. The block diagram 1500, themethod 1600, and the block diagram 1700 may be practiced in otherapparatus. In addition to showing the block diagram 1500, FIG. 15 showshardware of the apparatus 100 that may execute the instructions of theblock diagram 1500. The hardware may include a processor 1502, and amemory 1504 storing machine readable instructions that when executed bythe processor cause the processor to perform the instructions of theblock diagram 1500. The memory 1504 may represent a non-transitorycomputer readable medium. FIG. 16 may represent an example method forimage content moderation, and the steps of the method. FIG. 17 mayrepresent a non-transitory computer readable medium 1702 having storedthereon machine readable instructions to provide image contentmoderation according to an example. The machine readable instructions,when executed, cause a processor 1704 to perform the instructions of theblock diagram 1700 also shown in FIG. 17.

The processor 1502 of FIG. 15 and/or the processor 1704 of FIG. 17 mayinclude a single or multiple processors or other hardware processingcircuit, to execute the methods, functions and other processes describedherein. These methods, functions and other processes may be embodied asmachine readable instructions stored on a computer readable medium,which may be non-transitory (e.g., the non-transitory computer readablemedium 1702 of FIG. 17), such as hardware storage devices (e.g., RAM(random access memory), ROM (read only memory), EPROM (erasable,programmable ROM), EEPROM (electrically erasable, programmable ROM),hard drives, and flash memory). The memory 1504 may include a RAM, wherethe machine readable instructions and data for a processor may resideduring runtime.

Referring to FIGS. 1-15, and particularly to the block diagram 1500shown in FIG. 15, the memory 1504 may include instructions 1506 toclassify (e.g., by the object category classifier 114 that is executedby the at least one hardware processor), based on a learning model, anobject displayed in an image into a category of a plurality ofcategories.

The processor 1502 may fetch, decode, and execute the instructions 1508to detect (e.g., by the object category detector 120 that is executed bythe at least one hardware processor), based on another learning model,the object displayed in the image.

The processor 1502 may fetch, decode, and execute the instructions 1510to refine (e.g., by the object category detector 120 that is executed bythe at least one hardware processor) the detected object based on alabel associated with the detected object.

The processor 1502 may fetch, decode, and execute the instructions 1512to determine (e.g., by the object category detector 120 that is executedby the at least one hardware processor), based on the another learningmodel, a category of the plurality of categories for the refineddetected object.

The processor 1502 may fetch, decode, and execute the instructions 1514to identify (e.g., by the keyword category analyzer 126 that is executedby the at least one hardware processor), based on the label, a keywordassociated with the object displayed in the image from a plurality ofkeywords.

The processor 1502 may fetch, decode, and execute the instructions 1516to determine (e.g., by the keyword category analyzer 126 that isexecuted by the at least one hardware processor), based on theidentified keyword, a category of the plurality of categories for theobject displayed in the image.

The processor 1502 may fetch, decode, and execute the instructions 1518to categorize (e.g., by the rules-based categorizer 130 that is executedby the at least one hardware processor), based on a set of rules, theobject displayed in the image into a category of the plurality ofcategories.

The processor 1502 may fetch, decode, and execute the instructions 1520to moderate (e.g., by the fusion-based categorizer 134 that is executedby the at least one hardware processor) image content of the image bycategorizing, based on the classification of the object categoryclassifier xxx, the determination of the object category detector xxx,the determination of the keyword category analyzer xxx, and thecategorization of the rules-based categorizer xxx, the object displayedin the image into a category of the plurality of categories. In thisregard, the processor 1502 may fetch, decode, and execute theinstructions 1520 to moderate (e.g., by the fusion-based categorizer 134that is executed by the at least one hardware processor) image contentof the image by categorizing, based on the classification, based on thelearning model, of the object displayed in an image into the category ofthe plurality of categories, the determination, based on the anotherlearning model, of the category of the plurality of categories for therefined detected object, the determination, based on the identifiedkeyword, of the category of the plurality of categories for the objectdisplayed in the image, and the categorization, based on the set ofrules, of the object displayed in the image into the category of theplurality of categories, the object displayed in the image into acategory of the plurality of categories.

Referring to FIGS. 1-14 and 16, and particularly FIG. 16, for the method1600, at block 1602, the method may include classifying, by the objecttagging classifier 138 that is executed by at least one hardwareprocessor, based on a learning model, an object displayed in an imageinto a category of a plurality of categories.

At block 1604, the method may include determining, by the color tagger144 that is executed by the at least one hardware processor, colorinformation from the object.

At block 1606, the method may include determining, by the color tagger144 that is executed by the at least one hardware processor, a pluralityof color tags associated with the object.

At block 1608, the method may include determining, by the color tagger144 that is executed by the at least one hardware processor, a pluralityof color distances between the color information and the plurality ofcolor tags.

At block 1610, the method may include determining, by the color tagger144 that is executed by the at least one hardware processor, a color tagof the plurality of color tags that is to be assigned to the objectbased on a determination of a minimum color distance from the pluralityof color distances. The color tag may represent a color that is to beassigned to the object.

At block 1612, the method may include determining, by the keywordtagging analyzer 150 that is executed by the at least one hardwareprocessor, a category and a color associated with the object based on alabel associated with the object.

At block 1614, the method may include moderating, by the fusion-basedtagger 152 that is executed by the at least one hardware processor,image content of the image by tagging, based on the classification ofthe object tagging classifier, the determination of the color tagger,and the determination of the keyword tagging analyzer, the objectdisplayed in the image with a category and a color associated with theobject.

Referring to FIGS. 1-14 and 17, and particularly FIG. 17, for the blockdiagram 1700, the non-transitory computer readable medium 1702 mayinclude instructions 1706 to ascertain an image.

The processor 1704 may fetch, decode, and execute the instructions 1708to determine, for the image, a plurality of predetermined classes thatrepresent inappropriate content.

The processor 1704 may fetch, decode, and execute the instructions 1710to identify an object displayed in the image.

The processor 1704 may fetch, decode, and execute the instructions 1712to analyze the object with respect to each class of the plurality ofpredetermined classes.

The processor 1704 may fetch, decode, and execute the instructions 1714to classify, based on the analysis of the object with respect to eachclass of the plurality of predetermined classes, the object asappropriate or inappropriate.

The processor 1704 may fetch, decode, and execute the instructions 1716to determine whether the object is classified as inappropriate.

The processor 1704 may fetch, decode, and execute the instructions 1718to, in response to a determination that the object is classified asinappropriate, mark the object as inappropriate.

What has been described and illustrated herein is an example along withsome of its variations. The terms, descriptions and figures used hereinare set forth by way of illustration only and are not meant aslimitations. Many variations are possible within the spirit and scope ofthe subject matter, which is intended to be defined by the followingclaims—and their equivalents—in which all terms are meant in theirbroadest reasonable sense unless otherwise indicated.

What is claimed is:
 1. A non-transitory computer readable medium havingstored thereon machine readable instructions, the machine readableinstructions, when executed by at least one hardware processor, causethe at least one hardware processor to: ascertain an image; determine,for the image, a plurality of predetermined classes that representinappropriate content; identify an object displayed in the image;perform a threshold based analysis of an energy component related to theobject with respect to each class of the plurality of predeterminedclasses; classify, based on the analysis of the energy component relatedto the object with respect to each class of the plurality ofpredetermined classes, the object as appropriate or inappropriate;determine whether the object is classified as inappropriate; in responseto a determination that the object is classified as inappropriate, markthe object as inappropriate; ascertain a list of defaulter vendors;compare a vendor associated with the inappropriate object with the listof defaulter vendors; in response to a determination that the vendorassociated with the inappropriate object is on the list of defaultervendors, increase a defaulter probability associated with the vendorassociated with the inappropriate object; compare a total probabilitybased on the increase in the defaulter probability to a threshold; andin response to a determination that the total probability is greaterthan the threshold, mark the vendor associated with the inappropriateobject as being unauthorized to upload further images.
 2. Thenon-transitory computer readable medium according to claim 1, whereinthe instructions are further to cause the at least one hardwareprocessor to: train a learning model to analyze a region of the object;and analyze, based on the learning model, the object with respect toeach class of the plurality of predetermined classes.
 3. Thenon-transitory computer readable medium according to claim 1, whereinthe instructions to analyze the object with respect to each class of theplurality of predetermined classes, and classify, based on the analysisof the object with respect to each class of the plurality ofpredetermined classes, the object as appropriate or inappropriate, arefurther to cause the at least one hardware processor to: detect a faceof a person in the object; transform an image of the detected face intofrequency domain; determine the energy component of the transformedimage of the detected face; compare the determined energy component toa-another threshold; and in response to a determination that the energycomponent is below the other threshold, classify the object asinappropriate.
 4. The non-transitory computer readable medium accordingto claim 1, wherein the instructions are further to cause the at leastone hardware processor to: classify, based on a learning model, theobject displayed in the image into a category of a plurality ofcategories; detect, based on another learning model, the objectdisplayed in the image; refine the detected object based on a labelassociated with the detected object; determine, based on the anotherlearning model, a category of the plurality of categories for therefined detected object; identify, based on the label, a keywordassociated with the object displayed in the image from a plurality ofkeywords; determine, based on the identified keyword, a category of theplurality of categories for the object displayed in the image;categorize, based on a set of rules, the object displayed in the imageinto a category of the plurality of categories; and moderate imagecontent of the image by categorizing, based on the classification, basedon the learning model, of the object displayed in an image into thecategory of the plurality of categories, the determination, based on theanother learning model, of the category of the plurality of categoriesfor the refined detected object, the determination, based on theidentified keyword, of the category of the plurality of categories forthe object displayed in the image, and the categorization, based on theset of rules, of the object displayed in the image into the category ofthe plurality of categories, the object displayed in the image into acategory of the plurality of categories.
 5. The non-transitory computerreadable medium according to claim 4, wherein the instructions arefurther to cause the at least one hardware processor to: classify, basedon a further learning model, the object displayed in the image into afurther category of the plurality of further categories; determine colorinformation from the object; determine a plurality of color tagsassociated with the object; determine a plurality of color distancesbetween the color information and the plurality of color tags; determinea color tag of the plurality of color tags that is to be assigned to theobject based on a determination of a minimum color distance from theplurality of color distances, wherein the color tag represents a colorthat is to be assigned to the object; determine a yet further categoryand a yet further color associated with the object based on the labelassociated with the object; and moderate the image content of the imageby tagging, based on the classification, based on the further learningmodel, the object displayed in the image into the further category ofthe plurality of further categories, the determination of the color tagof the plurality of color tags, and the determination of the yet furthercategory and the yet further color associated with the object based onthe label associated with the object, the object displayed in the imagewith a category of the plurality of further categories and a colorassociated with the object.
 6. The non-transitory computer readablemedium according to claim 4, wherein the plurality of categories includemale, female, and neutral.
 7. The non-transitory computer readablemedium according to claim 1, wherein the object includes a clothingproduct.
 8. The non-transitory computer readable medium according toclaim 1, wherein the instructions are further to cause the at least onehardware processor to: train a learning model using a plurality ofcategorized images; and classify, based on the trained learning model,the object into the category of the plurality of categories.
 9. Thenon-transitory computer readable medium according to claim 5, whereinthe instructions to moderate the image content of the image by taggingare further to cause the at least one hardware processor to: moderate,based on a majority decision associated with the classification, basedon the further learning model, the object displayed in the image intothe further category of the plurality of further categories, thedetermination of the color tag of the plurality of color tags, and thedetermination of the yet further category and the yet further colorassociated with the object based on the label associated with theobject, the object displayed in the image with the category of theplurality of further categories and the color associated with theobject.
 10. The non-transitory computer readable medium according toclaim 4, wherein the instructions are further to cause the at least onehardware processor to: refine the classification of the object displayedin the image into the category of the plurality of categories.
 11. Anapparatus comprising: at least one processor; and a non-transitorycomputer readable medium storing machine readable instructions that whenexecuted by the at least one processor cause the at least one processorto: ascertain an image; determine, for the image, a plurality ofpredetermined classes that represent inappropriate content; identify anobject displayed in the image; perform a threshold based analysis of anenergy component related to the object with respect to each class of theplurality of predetermined classes; classify, based on the analysis ofthe energy component related to the object with respect to each class ofthe plurality of predetermined classes, the object as appropriate orinappropriate; determine whether the object is classified asinappropriate; in response to a determination that the object isclassified as inappropriate, mark the object as inappropriate; train alearning model to analyze a region of the object; analyze, based on thelearning model, the object with respect to each class of the pluralityof predetermined classes; classify, based on another learning model, theobject displayed in the image into a category of a plurality ofcategories; detect, based on a further learning model, the objectdisplayed in the image; refine the detected object based on a labelassociated with the detected object; determine, based on the furtherlearning model, a category of the plurality of categories for therefined detected object; identify, based on the label, a keywordassociated with the object displayed in the image from a plurality ofkeywords; determine, based on the identified keyword, a category of theplurality of categories for the object displayed in the image;categorize, based on a set of rules, the object displayed in the imageinto a category of the plurality of categories; and moderate imagecontent of the image by categorizing, based on the classification, basedon the other learning model, of the object displayed in an image intothe category of the plurality of categories, the determination, based onthe further learning model, of the category of the plurality ofcategories for the refined detected object, the determination, based onthe identified keyword, of the category of the plurality of categoriesfor the object displayed in the image, and the categorization, based onthe set of rules, of the object displayed in the image into the categoryof the plurality of categories, the object displayed in the image into acategory of the plurality of categories.
 12. A method comprising:ascertaining, by at least one hardware processor, an image; determining,by the at least one hardware processor, for the image, a plurality ofpredetermined classes that represent inappropriate content; identifying,by the at least one hardware processor, an object displayed in theimage; analyzing, by the at least one hardware processor, the objectwith respect to each class of the plurality of predetermined classes;classifying, by the at least one hardware processor, based on theanalysis of the object with respect to each class of the plurality ofpredetermined classes, the object as appropriate or inappropriate by:detecting a face of a person in the object; transforming an image of thedetected face into frequency domain; determining an energy component ofthe transformed image of the detected face; comparing the determinedenergy component to a threshold; and in response to a determination thatthe energy component is below the threshold, classifying the object asinappropriate; determining, by the at least one hardware processor,whether the object is classified as inappropriate; and in response to adetermination that the object is classified as inappropriate, marking,by the at least one hardware processor, the object as inappropriate.